A survey of parallel distributed genetic algorithms

In this work we review the most important existing developments and future trends in the class of Parallel Genetic Algorithms (PGAs). PGAs are mainly subdivided into coarse and fine grain PGAs, the coarse grain models being the most popular ones. An exceptional characteristic of PGAs is that they are not just the parallel version of a sequential algorithm intended to provide speed gains. Instead, they represent a new kind of meta-heuristics of higher efficiency and efficacy thanks to their structured population and parallel execution. The good robustness of these algorithms on problems of high complexity has led to an increasing number of applications in the fields of artificial intelligence, numeric and combinatorial optimization, business, engineering, etc. We make a formalization of these algorithms, and present a timely and topic survey of their most important traditional and recent technical issues. Besides that, useful summaries on their main applications plus Internet pointers to important web sites are included in order to help new researchers to access this growing area.